技术领域Technical Field
本发明涉及公路规划领域，涉及自动驾驶车辆混入下的公路货车专用道设置条件评价方法。The present invention relates to the field of highway planning, and more particularly to a method for evaluating the conditions for setting up a dedicated highway truck lane when an autonomous driving vehicle is mixed in.
背景技术Background Art
随着我国货物运输需求的持续增长，公路货运以其“门到门”的运输优势愈发重要，公路货运交通组织与管理技术成为交通运输领域的核心课题之一。通过设置货车专用车道将客货混合交通流分隔为独立的客车交通流和货车交通流，能够显著降低由于车重、尺寸、动力性能，驾驶行为等方面的差异而引发的安全问题，同时提高公路通行效率。但随着自动驾驶技术的广泛应用，其为未来公路道路设计带来新的可能性，为公路路网的交通组织与管理提出了新的挑战。With the continuous growth of my country's freight transportation demand, road freight has become increasingly important with its "door-to-door" transportation advantage, and road freight traffic organization and management technology has become one of the core topics in the field of transportation. By setting up dedicated truck lanes to separate passenger and freight mixed traffic flows into independent passenger traffic flows and truck traffic flows, it can significantly reduce safety issues caused by differences in vehicle weight, size, power performance, driving behavior, etc., while improving highway traffic efficiency. However, with the widespread application of autonomous driving technology, it brings new possibilities for future highway design and poses new challenges to the traffic organization and management of highway networks.
道路设置的评价研究，通常基于交通流理论建立车均延误模型，通过车均延误模型估计车均延误，进而作为交通管理和控制理论研究的依据。Evaluation research on road settings usually establishes an average vehicle delay model based on traffic flow theory, estimates the average vehicle delay through the average vehicle delay model, and then serves as the basis for research on traffic management and control theory.
目前研究建立的车均延误评价模型通常用于两类道路交通场景：a.城市信号控制交叉口，常用的车均延误模型包括Webster延误模型、ARRB延误模型和HCM延误模型等，模型通常包含车均延误与信号灯配时参数等之间的关联关系，对公路路网不适用；b.交通事故、施工区域、公交车和行人等干扰影响下的交通流，所用的车均延误评价模型仅适用特定的交通场景，且在模型构建过程中主要考虑交通流量与道路通行能力的比例、车辆的到达-驶离规律等因素。The average vehicle delay evaluation models currently established are usually used in two types of road traffic scenarios: a. Urban signal-controlled intersections. Commonly used average vehicle delay models include the Webster delay model, the ARRB delay model, and the HCM delay model. The models usually include the correlation between the average vehicle delay and signal light timing parameters, etc., and are not applicable to highway networks; b. Traffic flows affected by traffic accidents, construction areas, buses, pedestrians, etc. The average vehicle delay evaluation model used is only applicable to specific traffic scenarios, and the main factors considered in the model construction process are the ratio of traffic flow to road capacity, the arrival-departure pattern of vehicles, and other factors.
目前建立的车均延误评价模型未考虑自动驾驶客车和自动驾驶货车同时混入对交通流的影响；在未来，传统人工驾驶客车、人工驾驶货车与自动驾驶客车、自动驾驶货车组成的混合交通流更为复杂；因此，当前的车均延误评价模型不能直接反映自动驾驶车辆混入对公路路网车均延误造成的影响，更难以评价公路货车专用车道设置方式对混合交通流的适应性。The currently established average vehicle delay evaluation model does not take into account the impact of the simultaneous mixing of self-driving buses and self-driving trucks on traffic flow; in the future, the mixed traffic flow composed of traditional manually-driven buses, manually-driven trucks, self-driving buses and self-driving trucks will be more complex; therefore, the current average vehicle delay evaluation model cannot directly reflect the impact of the mixing of self-driving vehicles on the average vehicle delay of the highway network, and it is even more difficult to evaluate the adaptability of the setting method of dedicated lanes for highway trucks to mixed traffic flow.
发明内容Summary of the invention
针对上述问题，本发明提供了自动驾驶车辆混入下的公路货车专用道设置条件评价方法，建立路网车均延误的专用车道设置方式评价模型，通过构建以交通量水平、货车占比和自动驾驶渗透率为影响因素的不同交通流条件来验证评价模型的有效性，对比不同专用车道设置方式的通行效率，得出不同专用车道设置方式下各因素对路网车均延误的影响趋势，以及各类交通条件下最适用的专用车道设置方式。In response to the above problems, the present invention provides a method for evaluating the setting conditions of highway truck lanes with the mixing of autonomous driving vehicles, establishes an evaluation model for the setting methods of dedicated lanes with average vehicle delays in the road network, verifies the effectiveness of the evaluation model by constructing different traffic flow conditions with traffic volume level, truck proportion and autonomous driving penetration rate as influencing factors, compares the traffic efficiency of different dedicated lane setting methods, and obtains the influence trend of various factors on the average vehicle delays in the road network under different dedicated lane setting methods, as well as the most suitable dedicated lane setting method under various traffic conditions.
为实现上述目的，本发明提供了自动驾驶车辆混入下的公路货车专用道设置条件评价方法，包括：建立公路货车专用道设置条件评价模型：To achieve the above-mentioned purpose, the present invention provides a method for evaluating the conditions for setting up a dedicated lane for trucks on a highway with the mixing of autonomous driving vehicles, comprising: establishing a model for evaluating the conditions for setting up a dedicated lane for trucks on a highway:
d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4，其中，d表示路网车均延误，a1、a2、a3、a4分别表示公路自动驾驶货车交通量、公路人工驾驶货车交通量、公路自动驾驶客车交通量、公路人工驾驶客车交通量，D0表示常量参数，D1、D2、D3、D4、D5、D6均表示与所在路段的饱和交通量和干扰系数相关的延误系数；d＝D0 +D1 a1 a2 +D2 a1 a3 +D3 a1 a4 +D4 a2 a3 +D5 a2 a4 +D6 a3 a4 , where d represents the average delay of all vehicles in the road network, a1 , a2 , a3 , and a4 represent the traffic volume of autonomous driving trucks on the highway, the traffic volume of manually driven trucks on the highway, the traffic volume of autonomous driving buses on the highway, and the traffic volume of manually driven buses on the highway, respectively; D0 represents a constant parameter, and D1 , D2 , D3 , D4 , D5 , and D6 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section;
利用历史路网车均延误数据或仿真实验得到的设置自动驾驶货车专用道、货车专用道和不设置专用道的路网车均延误数据，分别对三种设置方式下所述评价模型中D0、D1、D2、D3、D4、D5、D6进行标定，得到三个完整评价模型；Using the historical average vehicle delay data of the road network or the average vehicle delay data of the road network with dedicated lanes for autonomous driving trucks, dedicated lanes for trucks, and no dedicated lanes obtained from simulation experiments, D0 , D1 , D2 , D3 , D4 , D5 , and D6 in the evaluation model under the three settings are calibrated respectively to obtain three complete evaluation models;
采集路网的交通量水平、货车占比和自动驾驶渗透率的取值，并将所述交通量水平、货车占比和自动驾驶渗透率的取值作为自变量分别输入三个完整评价模型；Collecting the values of the traffic volume level, truck proportion and autonomous driving penetration rate of the road network, and inputting the values of the traffic volume level, truck proportion and autonomous driving penetration rate as independent variables into three complete evaluation models respectively;
对比路网在该交通量水平、货车占比和自动驾驶渗透率的情况下，三种货车专用车道设置方案的路网车均延误的值，将路网车均延误的值最小的方案作为该情况下路网的货车专用车道设置方案。By comparing the average network vehicle delay values of the three truck-only lane setting schemes under the conditions of traffic volume level, truck proportion and autonomous driving penetration rate, the scheme with the smallest average network vehicle delay value is taken as the truck-only lane setting scheme for the road network under this situation.
作为本发明的进一步改进，As a further improvement of the present invention,
所述将所述交通量水平、货车占比和自动驾驶渗透率的取值作为自变量输入所述完整评价模型；包括：The step of inputting the values of the traffic volume level, truck proportion and autonomous driving penetration rate as independent variables into the complete evaluation model comprises:
根据所述交通量水平、货车占比和自动驾驶渗透率计算可得a1、a2、a3、a4，具体为：According to the traffic volume level, truck proportion and autonomous driving penetration rate, a1 , a2 , a3 and a4 can be calculated as follows:
交通量水平*货车占比＝货车交通量；Traffic volume level* truck share = truck traffic volume;
交通量水平*(1-货车占比)＝客车交通量Traffic volume level*(1-truck ratio)=passenger vehicle traffic volume
货车交通量*自动驾驶渗透率＝公路自动驾驶货车交通量＝a1；Truck traffic volume*autonomous driving penetration rate=highway autonomous driving truck traffic volume=a1 ;
货车交通量*(1-自动驾驶渗透率)＝公路人工驾驶货车交通量＝a2；Truck traffic volume*(1-autonomous driving penetration rate)=highway manually driven truck traffic volume=a2 ;
客车交通量*自动驾驶渗透率＝公路自动驾驶客车交通量＝a3；Passenger car traffic volume*Automated driving penetration rate=Highway automated driving passenger car traffic volume=a3 ;
客车交通量*(1-自动驾驶渗透率)＝公路人工驾驶客车交通量＝a4。Passenger car traffic volume*(1-automatic driving penetration rate)=highway manually driven passenger car traffic volume=a4 .
作为本发明的进一步改进，As a further improvement of the present invention,
基于微观交通仿真软件VISSIM及其COM二次开发接口构建了典型路网的仿真模型，进行仿真实验；Based on the micro traffic simulation software VISSIM and its COM secondary development interface, a simulation model of a typical road network was constructed and simulation experiments were carried out;
仿真模型包含三种专用车道设置方式，分别为：设置自动驾驶货车专用车道、设置货车专用车道、不设置货车专用车道；The simulation model includes three dedicated lane setting methods: setting a dedicated lane for autonomous driving trucks, setting a dedicated lane for trucks, and not setting a dedicated lane for trucks;
仿真模型设置覆盖各种交通需求水平的多个交通量水平、多个货车占比水平和多个自动驾驶渗透率水平。The simulation model is set up with multiple traffic volume levels, multiple truck share levels and multiple autonomous driving penetration levels covering various traffic demand levels.
作为本发明的进一步改进，As a further improvement of the present invention,
利用仿真实验得到的设置自动驾驶货车专用道、货车专用道和不设置专用道的路网车均延误数据；包括：The delay data of the road network with dedicated lanes for autonomous driving trucks, dedicated lanes for trucks, and no dedicated lanes obtained through simulation experiments; including:
仿真过程中，每一种货车专用道设置方式在每一组交通流条件下的仿真时长不少于7800s，且仿真评价时间从600s开始，对每组仿真模型运行不小于5次，并将多次仿真输出结果的平均值作为最终仿真结果。During the simulation process, the simulation time for each truck lane setting method under each set of traffic flow conditions is no less than 7800s, and the simulation evaluation time starts from 600s. Each set of simulation models is run no less than 5 times, and the average value of multiple simulation output results is taken as the final simulation result.
作为本发明的进一步改进，As a further improvement of the present invention,
所述建立公路货车专用道设置条件评价模型：The establishment of a highway truck lane setting condition evaluation model:
d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4；包括：d＝D0 +D1 a1 a2 +D2 a1 a3 +D3 a1 a4 +D4 a2 a3 +D5 a2 a4 +D6 a3 a4 ; including:
针对建立公路自动驾驶货车专用道、建立公路货车专用道和不建立货车专用道三种公路货车专用道设置方式，分别分析并建立路网车均延误计算公式；According to the three ways of setting up dedicated lanes for trucks on highways, namely, establishing dedicated lanes for self-driving trucks on highways, establishing dedicated lanes for trucks on highways, and not establishing dedicated lanes for trucks, the calculation formulas for average vehicle delay on the road network are analyzed and established respectively;
结合三种公路货车专用道设置方式的车均延误计算公式，建立评价模型。An evaluation model is established by combining the average vehicle delay calculation formula for the three types of highway truck lane settings.
作为本发明的进一步改进，As a further improvement of the present invention,
对于建立公路自动驾驶货车专用道的路网，车均延误包括匝道交织区的车均延误和主路的车均延误；For road networks with dedicated lanes for autonomous trucks, the average vehicle delay includes the average vehicle delay in the ramp weaving area and the average vehicle delay on the main road;
匝道交织区的车均延误为自动驾驶货车进入、离开专用车道的自动驾驶货车和主路上的人工驾驶货车、人工驾驶客车、自动驾驶客车间相互干扰产生的车均延误，计算公式为：The average vehicle delay in the ramp weaving zone is the average vehicle delay caused by the interference between the autonomous driving trucks entering and leaving the dedicated lanes and the manually driven trucks, manually driven buses, and autonomous driving buses on the main road. The calculation formula is:
da＝Da,0+Da,1a1a2+Da,2a1a3+Da,3a1a4da =Da,0 +Da,1 a1 a2 +Da,2 a1 a3 +Da,3 a1 a4
其中，Da,0表示常量参数，Da,1、Da,2和Da,3均表示与车流所在路段的饱和交通量和干扰系数相关的延误系数；Wherein, Da,0 represents a constant parameter,Da,1 , Da,2 and Da,3 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section where the traffic flow is located;
主路的车均延误为普通车道上人工驾驶货车、人工驾驶客车、自动驾驶客车相互干扰产生的车均延误，计算公式为：The average vehicle delay on the main road is the average vehicle delay caused by the interference between manual-driven trucks, manual-driven buses, and automatic-driven buses on the ordinary lanes. The calculation formula is:
dc＝Dc,0+Dc,1a2a3+Dc,2a2a4+Dc,3a3a4dc =Dc,0 +Dc,1 a2 a3 +Dc,2 a2 a4 +Dc,3 a3 a4
其中，Dc,0表示常量参数，Dc,1、Dc,2和Dc,3均表示与车流所在路段的饱和交通量和干扰系数相关的延误系数；Where Dc,0 represents a constant parameter, Dc,1 , Dc,2 and Dc,3 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section where the traffic flow is located;
自动驾驶货车专用道的路网车均延误，计算公式为：The average delay of all vehicles on the road network in the dedicated lane for autonomous trucks is calculated as follows:
d＝da+dcd＝da +dc
＝Da,0+Da,1a1a2+Da,2a1a3+Da,3a1a4+Dc,0+Dc,1a2a3+Dc,2a2a4+Dc,3a3a4＝Da,0 +Da,1 a1 a2 +Da,2 a1 a3 +Da,3 a1 a4 +Dc,0 +Dc,1 a2 a3 +Dc, 2 a2 a4 +Dc,3 a3 a4
可整理表示为：It can be organized as:
d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4d＝D0 +D1 a1 a2 +D2 a1 a3 +D3 a1 a4 +D4 a2 a3 +D5 a2 a4 +D6 a3 a4
其中，D0表示常量参数，D1、D2、D3、D4、D5和D6均表示与所在路段的饱和交通量和干扰系数相关的延误系数。Wherein, D0 represents a constant parameter, and D1 , D2 , D3 , D4 , D5 and D6 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section.
作为本发明的进一步改进，As a further improvement of the present invention,
对于建立公路货车专用道的路网，车均延误包括匝道交织区的车均延误和主路区的车均延误；For road networks with dedicated truck lanes, the average vehicle delay includes the average vehicle delay in the ramp weaving area and the average vehicle delay in the main road area;
匝道交织区车均延误为自动驾驶货车、人工驾驶货车进入/离开货车专用道与主路上的自动驾驶客车、人工驾驶客车相互干扰产生的车均延误，计算公式为：The average vehicle delay in the ramp weaving zone is the average vehicle delay caused by the interference between the self-driving trucks and manually driven trucks entering/leaving the truck lane and the self-driving passenger cars and manually driven passenger cars on the main road. The calculation formula is:
da＝Da,0+Da,1a1a3+Da,2a1a4+Da,3a2a3+Da,4a2a4da =Da,0 +Da,1 a1 a3 +Da,2 a1 a4 +Da,3 a2 a3 +Da,4 a2 a4
其中，Da,0表示常量参数，Da,1、Da,2、Da,3和Da,4均表示与车流所在路段的饱和交通量和干扰系数相关的延误系数；Wherein,Da,0 represents a constant parameter, Da,1 , Da,2 , Da,3 and Da,4 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section where the traffic flow is located;
主路区的车均延误包括公路货车专用道上自动驾驶货车、人工驾驶货车相互干扰产生的车均延误，共享车道上人工驾驶货车、人工驾驶客车相互干扰产生的车均延误，计算公式为：The average vehicle delay in the main road area includes the average vehicle delay caused by the interference between self-driving trucks and manually driven trucks on the highway truck lane, and the average vehicle delay caused by the interference between manually driven trucks and manually driven buses on the shared lane. The calculation formula is:
dc＝Dc,0+Dc,1a1a2+Dc,2a3a4dc =Dc,0 +Dc,1 a1 a2 +Dc,2 a3 a4
其中，Dc,0表示常量参数，Dc,1和Dc,2均表示与车流所在路段的饱和交通量和干扰系数相关的延误系数；Among them, Dc,0 represents a constant parameter, Dc,1 and Dc,2 both represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section where the traffic flow is located;
货车专用道的路网车均延误，计算公式为：The average delay of all vehicles in the truck lane is calculated as follows:
d＝da+dcd＝da +dc
＝Da,0+Da,1a1a3+Da,2a1a4+Da,3a2a3+Da,4a2a4+Dc,0+Dc,1a1a2+Dc,2a3a4＝Da,0 +Da,1 a1 a3 +Da,2 a1 a4 +Da,3 a2 a3 +Da,4 a2 a4 +Dc,0 +Dc, 1 a1 a2 +Dc,2 a3 a4
可整理表示为：It can be organized as:
d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4。d＝D0 +D1 a1 a2 +D2 a1 a3 +D3 a1 a4 +D4 a2 a3 +D5 a2 a4 +D6 a3 a4 .
作为本发明的进一步改进，对于不建立公路货车专用道的路网，匝道交织区无车辆进入/离开专用车道的需求，匝道交织区的车均延误可表达为Dc＝0；主路区的车均延误为自动驾驶客车、人工驾驶客车、自动驾驶货车、人工驾驶货车间交织影响产生的车均延误，计算公式为：As a further improvement of the present invention, for a road network without dedicated lanes for highway trucks, there is no demand for vehicles to enter/leave the dedicated lanes in the ramp weaving area, and the average vehicle delay in the ramp weaving area can be expressed as Dc =0; the average vehicle delay in the main road area is the average vehicle delay caused by the weaving effect between the self-driving passenger car, the manually driven passenger car, the self-driving truck, and the manually driven truck, and the calculation formula is:
d＝Dc,0+Dc,1a1a2+Dc,2a1a3+Dc,3a1a3+Dc,4a2a3+Dc,5a2a3+Dc,6a3a4；d＝Dc,0 +Dc,1 a1 a2 +Dc,2 a1 a3 +Dc,3 a1 a3 +Dc,4 a2 a3 +Dc,5 a2 a3 +Dc,6 a3 a4 ;
其中，Dc,0表示常量参数，Dc,1、Dc,2、Dc,3、Dc,4、Dc,5和Dc,6均表示与车流所在路段的饱和交通量和干扰系数相关的延误系数；Where Dc,0 represents a constant parameter, Dc,1 , Dc,2 , Dc,3 , Dc,4 , Dc,5 and Dc,6 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section where the traffic flow is located;
可整理表示为：It can be organized as:
d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4d＝D0 +D1 a1 a2 +D2 a1 a3 +D3 a1 a4 +D4 a2 a3 +D5 a2 a4 +D6 a3 a4
其中，D0表示常量参数，D1、D2、D3、D4、D5和D6均表示与所在路段的饱和交通量和干扰系数相关的延误系数。Wherein, D0 represents a constant parameter, and D1 , D2 , D3 , D4 , D5 and D6 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section.
与现有技术相比，本发明的有益效果为：Compared with the prior art, the present invention has the following beneficial effects:
本发明考虑到未来自动驾驶车辆的发展，人工驾驶客车、人工驾驶货车与自动驾驶客车、自动驾驶货车组成的混合交通流更为复杂，提供自动驾驶车辆混入下的公路货车专用道设置条件评价方法，构建以交通量水平、货车占比和自动驾驶渗透率为影响因素的车均延误模型，并以不同交通流条件来标定车均延误模型的参数，应用标定后的车均延误模型计算路网车均延误，车均延误越大路网通行效率越低，基于计算得到的路网车均延误对比不同专用车道设置方式的路网通行效率，得出不同专用车道设置方式下各因素对路网车均延误的影响趋势，以及各类交通条件下最适用的专用车道设置方式，最终，提高公路通行效率。The present invention takes into account the development of autonomous driving vehicles in the future. The mixed traffic flow consisting of manually driven buses, manually driven trucks, autonomous driving buses and autonomous driving trucks is more complicated. The present invention provides a method for evaluating the setting conditions of highway truck lanes under the mixing of autonomous driving vehicles, constructs an average vehicle delay model with traffic volume level, truck proportion and autonomous driving penetration rate as influencing factors, calibrates the parameters of the average vehicle delay model with different traffic flow conditions, and uses the calibrated average vehicle delay model to calculate the average vehicle delay of the road network. The greater the average vehicle delay, the lower the road network traffic efficiency. Based on the calculated average vehicle delay of the road network, the road network traffic efficiency of different dedicated lane setting methods is compared, and the influence trend of various factors on the average vehicle delay of the road network under different dedicated lane setting methods is obtained, as well as the most suitable dedicated lane setting method under various traffic conditions, ultimately improving the highway traffic efficiency.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明一种实施例公开的道路通行空间的区域划分示意图；FIG1 is a schematic diagram of the regional division of a road passage space disclosed in an embodiment of the present invention;
图2为本发明一种实施例公开的设置自动驾驶货车专用车道的路网交通；流分布示意图FIG. 2 is a schematic diagram of a road network traffic flow distribution for setting up a dedicated lane for self-driving trucks disclosed in an embodiment of the present invention;
图3为本发明一种实施例公开的仿真实验路网空间条件示意图；FIG3 is a schematic diagram of spatial conditions of a simulation experiment road network disclosed in an embodiment of the present invention;
图4为本发明一种实施例公开的三种专用车道设置方式的路网车均延误分布情况示意图；FIG4 is a schematic diagram of the average vehicle delay distribution of the road network for three dedicated lane setting methods disclosed in an embodiment of the present invention;
图5为本发明一种实施例公开的交通量水平为饱和交通量的20％条件下，三种专用车道设置方式的路网车均延误分布情况示意图；5 is a schematic diagram of the average vehicle delay distribution of the road network in three dedicated lane setting modes under the condition that the traffic volume level is 20% of the saturated traffic volume disclosed in an embodiment of the present invention;
图6为本发明一种实施例公开的交通量水平为饱和交通量的60％条件下，三种专用车道设置方式的路网车均延误分布情况示意图；FIG6 is a schematic diagram of the average vehicle delay distribution of the road network in three dedicated lane setting modes under the condition that the traffic volume level is 60% of the saturated traffic volume disclosed in an embodiment of the present invention;
图7为本发明一种实施例公开的交通量水平为饱和交通量的100％条件下，三种专用车道设置方式的路网车均延误分布情况示意图；7 is a schematic diagram of the average vehicle delay distribution of the road network in three dedicated lane setting modes under the condition that the traffic volume level is 100% of the saturated traffic volume disclosed in an embodiment of the present invention;
图8为本发明一种实施例公开的路网车均延误数据拟合结果FIG8 is a diagram showing the fitting result of the average vehicle delay data of the road network disclosed in an embodiment of the present invention
图9为本发明一种实施例公开的本发明评价模型、多元线性模型及对数回归模型对路网车均延误的拟合效果对比图。FIG9 is a comparison diagram of the fitting effects of the evaluation model of the present invention, the multivariate linear model and the logarithmic regression model on the average vehicle delay of the road network disclosed in an embodiment of the present invention.
具体实施方式DETAILED DESCRIPTION
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
下面结合附图对本发明做进一步的详细描述：The present invention is further described in detail below in conjunction with the accompanying drawings:
如图1所示，当在道路通行空间中设置专用车道时，如何评价专用车道的设置条件，本发明提供的自动驾驶车辆混入下的公路货车专用道设置条件评价方法，包括步骤：As shown in FIG1 , when a dedicated lane is set in a road traffic space, how to evaluate the setting conditions of the dedicated lane? The present invention provides a method for evaluating the setting conditions of a highway truck dedicated lane with the mixing of an autonomous driving vehicle, including the steps of:
S1、建立公路货车专用道设置条件评价模型：S1. Establish a model for evaluating the conditions for setting up dedicated highway truck lanes:
d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4，其中，d表示路网车均延误，a1、a2、a3、a4分别表示公路自动驾驶货车交通量、公路人工驾驶货车交通量、公路自动驾驶客车交通量、公路人工驾驶客车交通量，D0表示常量参数，D1、D2、D3、D4、D5、D6均表示与所在路段的饱和交通量和干扰系数相关的延误系数；d＝D0 +D1 a1 a2 +D2 a1 a3 +D3 a1 a4 +D4 a2 a3 +D5 a2 a4 +D6 a3 a4 , where d represents the average delay of all vehicles in the road network, a1 , a2 , a3 , and a4 represent the traffic volume of autonomous driving trucks on the highway, the traffic volume of manually driven trucks on the highway, the traffic volume of autonomous driving buses on the highway, and the traffic volume of manually driven buses on the highway, respectively; D0 represents a constant parameter, and D1 , D2 , D3 , D4 , D5 , and D6 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section;
其中，in,
针对建立公路自动驾驶货车专用道、建立公路货车专用道和不建立货车专用道三种公路货车专用道设置方式，分别分析并建立路网车均延误计算公式；According to the three ways of setting up dedicated lanes for trucks on highways, namely, establishing dedicated lanes for self-driving trucks on highways, establishing dedicated lanes for trucks on highways, and not establishing dedicated lanes for trucks, the calculation formulas for average vehicle delay on the road network are analyzed and established respectively;
结合三种公路货车专用道设置方式的车均延误计算公式，建立评价模型。An evaluation model is established by combining the average vehicle delay calculation formula for the three types of highway truck lane settings.
具体的，Specifically,
车均延误是指由于交通干扰、交通管理和控制设施等诸多因素造成车辆行驶时间额外损失的平均值，即指实际的行驶时间与理论行驶时间之差的平均值，理论行驶时间是指车辆以自由流车速平稳通过该路段的时间。The average vehicle delay refers to the average value of the additional loss of vehicle driving time due to many factors such as traffic interference, traffic management and control facilities, that is, the average value of the difference between the actual driving time and the theoretical driving time. The theoretical driving time refers to the time it takes for a vehicle to pass through the road section smoothly at the free flow speed.
车均延误计算公式为：The formula for calculating the average vehicle delay is:
式中，d表示路网车均延误，Q表示路网总交通量，n表示车辆序号，vn表示车辆n通过路网的实际车速，vn,t表示车辆n的自由流车速，l表示车辆n在路网行驶的总距离。Where d represents the average delay of all vehicles in the road network, Q represents the total traffic volume of the road network, n represents the vehicle number,vn represents the actual speed of vehicle n passing through the road network, vn,t represents the free-flow speed of vehicle n, and l represents the total distance traveled by vehicle n in the road network.
在分析交通流条件对车均延误的影响时，道路空间条件不变，可以认为车辆n在路网行驶的总距离l和自由流车速vn,t是与交通流条件无关的变量。因此，可以将路网车均延误模型抽象为路网总交通量Q和实际车速vn的表达式为：When analyzing the impact of traffic flow conditions on average vehicle delay, the road space conditions remain unchanged, and the total distance l traveled by vehicle n in the road network and the free flow speed vn,t can be considered as variables unrelated to traffic flow conditions. Therefore, the average vehicle delay model of the road network can be abstracted into the expression of the total traffic volume Q of the road network and the actual speed vn :
d＝f(Q,vn|vn,t,l)d＝f(Q,vn |vn,t ,l)
在混合交通流条件下，假定同一车型所采用的驾驶行为模型与参数相同，则车辆的实际车速主要受到不同车型间的驾驶行为差异性的干扰，不同车型间相互干扰造成的车均延误可用共享道路空间的不同车型的交通量乘积表示，公式为：。Under mixed traffic flow conditions, assuming that the driving behavior model and parameters used by the same vehicle model are the same, the actual vehicle speed is mainly affected by the differences in driving behaviors between different vehicle models. The average vehicle delay caused by mutual interference between different vehicle models can be expressed by the product of the traffic volumes of different vehicle models sharing the road space. The formula is:.
dx,y＝τaxaydx,y = τax ay
式中，dx,y表示车型为x的车流与车型为y的车流相互干扰造成的车均延误，τ表示不同车型间的干扰系数，ax表示车型为x的车流量，ay表示车型为y的车流量。Where dx,y represents the average vehicle delay caused by the interference between the vehicle flow of vehicle type x and the vehicle flow of vehicle type y, τ represents the interference coefficient between different vehicle types,ax represents the vehicle flow of vehicle type x, anday represents the vehicle flow of vehicle type y.
设置专用车道的公路路网，通过规定特定车辆可行驶的道路空间，可减少不同车型间相互干扰引起的延误，提高道路整体运行效率。但由于在入口匝道、出口匝道处专用车道的道路使用者驶入、驶出需求，不可避免地受到进、出专用车道的车流与普通车道的车流间的相互干扰影响，进而引起延误。由此，本发明将根据道路通行空间，分别对路段通行区和匝道交织区的路网车均延误进行评价，如图1所示。匝道交织区主要考虑进、出专用车道的车流与普通车道的车流间的相互干扰影响，路段通行区主要考虑共享道路空间的不同车型的车流间的相互干扰影响。The highway network with dedicated lanes can reduce the delays caused by mutual interference between different vehicle types and improve the overall road operation efficiency by specifying the road space where specific vehicles can travel. However, due to the demand of road users to enter and exit the dedicated lanes at the entrance ramp and exit ramp, it is inevitable that the traffic entering and exiting the dedicated lanes and the traffic in the ordinary lanes will be affected by the mutual interference, which will cause delays. Therefore, the present invention will evaluate the average delay of the road network vehicles in the section passage area and the ramp interweaving area according to the road passage space, as shown in Figure 1. The ramp interweaving area mainly considers the mutual interference between the traffic entering and exiting the dedicated lanes and the traffic in the ordinary lanes, and the section passage area mainly considers the mutual interference between the traffic of different vehicle types sharing the road space.
路段通行区和匝道交织区的交通流组织方式描述如下：在路段通行区，专用车道与相邻的普通车道间施划白实线，用于分隔专用车道的道路使用者及其他类型的道路使用者。在入口匝道下游一定范围施划白虚线，允许计划驶入专用车道的道路使用者在入口匝道后的一定范围内并入专用车道。同理，在出口匝道上游一定范围施划白虚线，允许计划驶离专用车道的道路使用者提前一定距离向右侧并道。The traffic flow organization method of the section traffic area and the ramp interweaving area is described as follows: In the section traffic area, a solid white line is drawn between the dedicated lane and the adjacent ordinary lane to separate the road users of the dedicated lane from other types of road users. A white dotted line is drawn within a certain range downstream of the entrance ramp to allow road users who plan to enter the dedicated lane to merge into the dedicated lane within a certain range after the entrance ramp. Similarly, a white dotted line is drawn within a certain range upstream of the exit ramp to allow road users who plan to leave the dedicated lane to merge to the right a certain distance in advance.
设定路网中某一行驶方向的自动驾驶货车交通量a1、人工驾驶货车交通量a2、自动驾驶客车交通量a3和人工驾驶客车交通量a4，公式表示为：Assuming the traffic volume of self-driving trucks a1 , the traffic volume of manually driven trucks a2 , the traffic volume of self-driving buses a3 , and the traffic volume of manually driven buses a4 in a certain driving direction in the road network, the formula is expressed as:
a1＝A1qαta1 =A1 qαt
a2＝A2q(1-α)ta2 =A2 q(1-α)t
a3＝A3qα(1-t)a3 =A3 qα(1-t)
a4＝A4q(1-α)(1-t)a4 =A4 q(1-α)(1-t)
式中，q表示实际交通流量占饱和交通量的比例、t表示货车占比，α表示自动驾驶渗透率，并假设货车和客车两类车型的自动驾驶渗透率相同。A1、A2、A3和A4均为常量参数，表示车流所在路段的饱和交通量。Where q represents the proportion of actual traffic flow to saturated traffic flow, t represents the proportion of trucks, α represents the penetration rate of autonomous driving, and it is assumed that the penetration rates of autonomous driving for trucks and buses are the same.A 1 , A2 , A3 and A4 are all constant parameters, representing the saturated traffic volume of the road section where the traffic flow is located.
对于设置自动驾驶货车专用车道的路网而言，匝道交织区主要由进入/离开专用车道的自动驾驶货车和主路上的其他三类车辆间相互干扰产生的车均延误，如图2所示。匝道交织区的车均延误为自动驾驶货车与三类车辆间相互干扰产生的车均延误之和，计算公式为：For a road network with dedicated lanes for autonomous trucks, the average vehicle delay in the ramp weaving area is mainly caused by the interference between the autonomous trucks entering/leaving the dedicated lanes and the other three types of vehicles on the main road, as shown in Figure 2. The average vehicle delay in the ramp weaving area is the sum of the average vehicle delays caused by the interference between the autonomous trucks and the three types of vehicles, and the calculation formula is:
da＝Da,0+Da,1a1a2+Da,2a1a3+Da,3a1a4da =Da,0 +Da,1 a1 a2 +Da,2 a1 a3 +Da,3 a1 a4
主路的车均延误主要由普通车道共享道路空间的三类车辆间相互干扰产生，为三类车辆间相互干扰产生的车均延误之和，计算公式为：The average vehicle delay on the main road is mainly caused by the mutual interference between the three types of vehicles sharing the road space in the ordinary lane. It is the sum of the average vehicle delay caused by the mutual interference between the three types of vehicles. The calculation formula is:
dc＝Dc,0+Dc,1a2a3+Dc,2a2a4+Dc,3a3a4dc =Dc,0 +Dc,1 a2 a3 +Dc,2 a2 a4 +Dc,3 a3 a4
匝道交织区与路段通行区的车均延误之和为路网车均延误，由此该专用车道设置方案下的路网车均延误可用公式表示为：The sum of the average vehicle delays in the ramp weaving area and the road section traffic area is the average vehicle delay of the road network. Therefore, the average vehicle delay of the road network under this dedicated lane setting scheme can be expressed by the formula:
d＝da+dcd＝da +dc
式中，da表示匝道交织区的车均延误，dc表示路段通行区的车均延误，d表示路网车均延误，Da,0和Dc,0表示常量参数，Da,1、Da,2、Da,3、Dc,1、Dc,2和Dc,3均表示与车流所在路段的饱和交通量和干扰系数相关的延误系数。whereda represents the average vehicle delay in the ramp weaving area,dc represents the average vehicle delay in the section traffic area, d represents the average vehicle delay in the road network, Da,0 andDc,0 represent constant parameters, Da,1 , Da,2 , Da,3 ,Dc,1 ,Dc,2 and Dc,3 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the section where the traffic flow is located.
同理，对于设置货车专用车道的路网而言，匝道交织区主要由进入/离开专用车道的自动驾驶货车、人工驾驶货车和主路上的其他两类车辆相互干扰产生的车均延误，匝道交织区的车均延误可用公式表示为：Similarly, for a road network with dedicated truck lanes, the average vehicle delay in the ramp weaving area is mainly caused by the interference between the self-driving trucks, manually driven trucks and other two types of vehicles on the main road entering/leaving the dedicated lanes. The average vehicle delay in the ramp weaving area can be expressed by the formula:
da＝Da,0+Da,1a1a3+Da,2a1a4+Da,3a2a3+Da,4a2a4da =Da,0 +Da,1 a1 a3 +Da,2 a1 a4 +Da,3 a2 a3 +Da,4 a2 a4
路段通行区的车均延误可用公式表示为：The average vehicle delay in the road section can be expressed as:
dc＝Dc,0+Dc,1a1a2+Dc,2a3a4dc =Dc,0 +Dc,1 a1 a2 +Dc,2 a3 a4
该方案的车均延误同样可用公式表示为：The average vehicle delay of this scheme can also be expressed by the formula:
d＝Dc,0+Dc,1a1a2+Dc,2a1a3+Dc,3a1a3+Dc,4a2a3+Dc,5a2a3+Dc,6a3a4d＝Dc,0 +Dc,1 a1 a2 +Dc,2 a1 a3 +Dc,3 a1 a3 +Dc,4 a2 a3 +Dc,5 a2 a3 +Dc,6 a3 a4
式中，Da,4、Dc,4、Dc,5、Dc,6同样表示与车流所在路段的饱和交通量和干扰系数相关的延误系数。Wherein, Da,4 ,Dc,4 ,Dc,5 andDc,6 also represent the delay coefficients related to the saturated traffic volume and interference coefficient of the road section where the traffic flow is located.
对于无专用车道而言，匝道交织区无车辆进入/离开专用车道的需求，匝道交织区的车均延误可表达为Dc＝0，主路上的四类车型间交织影响的车均延误可表达为：For the case of no dedicated lanes, there is no demand for vehicles to enter/leave the dedicated lanes in the ramp weaving area. The average vehicle delay in the ramp weaving area can be expressed as Dc = 0. The average vehicle delay affected by the weaving of the four types of vehicles on the main road can be expressed as:
d＝Dc,0+Dc,1a1a2+Dc,2a1a3+Dc,3a1a3+Dc,4a2a3+Dc,5a2a3+Dc,6a3a4，d＝Dc,0 +Dc,1 a1 a2 +Dc,2 a1 a3 +Dc,3 a1 a3 +Dc,4 a2 a3 +Dc,5 a2 a3 +Dc,6 a3 a4 ,
d＝da+dc同样可用于表示该方案的路网车均延误。d=da +dc can also be used to represent the average vehicle delay in the road network of this plan.
综上，可以发现，三类专用车道设置方式的路网车均延误均可用公式：d＝D0+D1a1a2+D2a1a3+D3a1a4+D4a2a3+D5a2a4+D6a3a4表达。由此，本发明将该模型作为自动驾驶车辆混入下的公路货车专用车道设置方法评价模型。式中，D0表示常量参数，D1、D2、D3、D4、D5和D6均表示与所在路段的饱和交通量和干扰系数相关的延误系数。In summary, it can be found that the average delay of the road network vehicles in the three types of dedicated lane settings can be expressed by the formula: d = D0 + D1 a1 a2 + D2 a1 a3 + D3 a1 a4 + D4 a2 a3 + D5 a2 a4 + D6 a3 a4. Therefore, the present invention uses this model as an evaluation model for the dedicated lane setting method for highway trucks under the mixing of autonomous driving vehicles. In the formula, D0 represents a constant parameter, and D1 , D2 , D3 , D4 , D5 and D6 all represent delay coefficients related to the saturated traffic volume and interference coefficient of the road section.
S2、利用历史路网车均延误数据或仿真实验得到的设置自动驾驶货车专用道、货车专用道和不设置专用道的路网车均延误数据，分别对三种设置方式下评价模型中D0、D1、D2、D3、D4、D5、D6进行标定，得到三个完整评价模型；S2. Using the historical average vehicle delay data of the road network or the average vehicle delay data of the road network with dedicated lanes for autonomous driving trucks, dedicated lanes for trucks, and no dedicated lanes obtained from simulation experiments, D0 , D1 , D2 , D3 , D4 , D5 , and D6 in the evaluation models under the three settings are calibrated respectively to obtain three complete evaluation models;
例如：得到设置自动驾驶货车专用道的评价模型为：For example, the evaluation model for setting up a dedicated lane for autonomous driving trucks is:
D＝-25.78+3458.00×a1a2+1486.88×a1a3+1673.47×a1a4+1673.47×a2a3+1310.79×a2a4+910.83×a3a4D＝-25.78+3458.00×a1 a2 +1486.88×a1 a3 +1673.47×a1 a4 +1673.47×a2 a3 +1310.79×a2 a4 +910.83×a3 a4
进一步的，Further,
本发明基于微观交通仿真软件VISSIM及其COM二次开发接口构建了典型路网的仿真模型，进行仿真实验；The present invention constructs a simulation model of a typical road network based on the microscopic traffic simulation software VISSIM and its COM secondary development interface, and conducts simulation experiments;
仿真模型包含三种专用车道设置方式，分别为：设置自动驾驶货车专用车道、设置货车专用车道、不设置货车专用车道；The simulation model includes three dedicated lane setting methods: setting a dedicated lane for autonomous driving trucks, setting a dedicated lane for trucks, and not setting a dedicated lane for trucks;
仿真模型设置覆盖各种交通需求水平的多个交通量水平、多个货车占比水平和多个自动驾驶渗透率水平。The simulation model is set up with multiple traffic volume levels, multiple truck share levels and multiple autonomous driving penetration levels covering various traffic demand levels.
利用仿真实验得到的设置自动驾驶货车专用道、货车专用道和不设置专用道的路网车均延误数据；包括：The delay data of the road network with dedicated lanes for autonomous driving trucks, dedicated lanes for trucks, and no dedicated lanes obtained through simulation experiments; including:
仿真过程中，每一种货车专用道设置方式在每一组交通流条件下的仿真时长不少于7800s，且仿真评价时间从600s开始，对每组仿真模型运行不小于5次，并将多次仿真出书结果的平均值作为最终仿真结果。During the simulation process, the simulation time for each truck lane setting method under each set of traffic flow conditions is no less than 7800s, and the simulation evaluation time starts from 600s. Each set of simulation models is run no less than 5 times, and the average value of multiple simulation results is taken as the final simulation result.
S3、采集路网的交通量水平、货车占比和自动驾驶渗透率的取值，并将交通量水平、货车占比和自动驾驶渗透率的取值作为自变量分别输入三个完整评价模型；S3. Collect the values of the traffic volume level, truck proportion and autonomous driving penetration rate of the road network, and input the values of the traffic volume level, truck proportion and autonomous driving penetration rate as independent variables into the three complete evaluation models respectively;
其中，将交通量水平、货车占比和自动驾驶渗透率的取值作为自变量输入完整评价模型；包括：The traffic volume level, truck proportion and autonomous driving penetration rate are used as independent variables to input into the complete evaluation model; including:
根据交通量水平、货车占比和自动驾驶渗透率计算可得a1、a2、a3、a4，具体为：According to the traffic volume level, truck proportion and autonomous driving penetration rate, a1 , a2 , a3 and a4 can be calculated as follows:
交通量水平*货车占比＝货车交通量；Traffic volume level* truck share = truck traffic volume;
交通量水平*(1-货车占比)＝客车交通量Traffic volume level*(1-truck ratio)=passenger vehicle traffic volume
货车交通量*自动驾驶渗透率＝公路自动驾驶货车交通量＝a1；Truck traffic volume*autonomous driving penetration rate=highway autonomous driving truck traffic volume=a1 ;
货车交通量*(1-自动驾驶渗透率)＝公路人工驾驶货车交通量＝a2；Truck traffic volume*(1-autonomous driving penetration rate)=highway manually driven truck traffic volume=a2 ;
客车交通量*自动驾驶渗透率＝公路自动驾驶客车交通量＝a3；Passenger car traffic volume*Automated driving penetration rate=Highway automated driving passenger car traffic volume=a3 ;
客车交通量*(1-自动驾驶渗透率)＝公路人工驾驶客车交通量＝a4。Passenger car traffic volume*(1-automatic driving penetration rate)=highway manually driven passenger car traffic volume=a4 .
S4、对比路网在该交通量水平、货车占比和自动驾驶渗透率的情况下，三种货车专用车道设置方案的路网车均延误的值，将路网车均延误的值最小的方案作为该情况下路网的货车专用车道设置方案。S4. Compare the average delay values of the three truck-only lane setting schemes under the conditions of the traffic volume level, truck proportion and autonomous driving penetration rate. The scheme with the smallest average delay value is taken as the truck-only lane setting scheme for the road network under this condition.
本发明基于微观交通仿真软件VISSIM及其COM二次开发接口构建了典型路网的仿真模型，进行仿真实验；The present invention constructs a simulation model of a typical road network based on the microscopic traffic simulation software VISSIM and its COM secondary development interface, and conducts simulation experiments;
仿真模型包含三种专用车道设置方式，分别为：设置自动驾驶货车专用车道、设置货车专用车道、不设置货车专用车道；The simulation model includes three dedicated lane setting methods: setting a dedicated lane for autonomous driving trucks, setting a dedicated lane for trucks, and not setting a dedicated lane for trucks;
仿真模型设置覆盖各种交通需求水平的多个交通量水平、多个货车占比水平和多个自动驾驶渗透率水平。The simulation model is set up with multiple traffic volume levels, multiple truck share levels and multiple autonomous driving penetration levels covering various traffic demand levels.
利用仿真实验得到的设置自动驾驶货车专用道、货车专用道和不设置专用道的路网车均延误数据；包括：The delay data of the road network with dedicated lanes for autonomous driving trucks, dedicated lanes for trucks, and no dedicated lanes obtained through simulation experiments; including:
仿真过程中，每一种货车专用道设置方式在每一组交通流条件下的仿真时长不少于7800s，且仿真评价时间从600s开始，对每组仿真模型运行不小于5次，并将多次仿真出书结果的平均值作为最终仿真结果。During the simulation process, the simulation time for each truck lane setting method under each set of traffic flow conditions is no less than 7800s, and the simulation evaluation time starts from 600s. Each set of simulation models is run no less than 5 times, and the average value of multiple simulation results is taken as the final simulation result.
实施例：Example:
基于微观交通仿真软件VISSIM及其COM二次开发接口构建典型路网的仿真模型，开展了专用车道设置条件评价。评价模型包含三种专用车道设置方式、五个交通量水平、五个货车占比水平和五个自动驾驶渗透率水平，实验次数总计375次。Based on the micro traffic simulation software VISSIM and its COM secondary development interface, a simulation model of a typical road network was constructed, and the conditions for setting up dedicated lanes were evaluated. The evaluation model includes three dedicated lane setting methods, five traffic volume levels, five truck share levels, and five autonomous driving penetration levels, with a total of 375 experiments.
(1)典型路网：(1) Typical road network:
选取典型的单向四车道的公路路段作为研究对象(如图3所示)，对象路段设置2个单车道入口匝道和两个单车道出口匝道，距主路起点的距离分别为0.7km、4.7km、5km、9km。入口匝道和出口匝道的变速车道、辅助车道与渐变段的长度设置符合公路工程行业标准《公路路线设计规范》(JTG D20-2017)和《公路立体交叉设计细则》(JTG/T D21-2014)的有关要求。匝道交织区长度设置为500m。主路的车道宽度设置为3.75m，限速为100km/h，匝道的车道宽度设置为3.75m，限速为40km/h。A typical one-way four-lane highway section was selected as the research object (as shown in Figure 3). The target section was set with two single-lane entrance ramps and two single-lane exit ramps, with the distances from the starting point of the main road being 0.7km, 4.7km, 5km, and 9km respectively. The lengths of the speed change lanes, auxiliary lanes, and gradient sections of the entrance ramps and exit ramps were set in accordance with the relevant requirements of the highway engineering industry standards "Highway Route Design Specifications" (JTG D20-2017) and "Highway Grade Separation Design Regulations" (JTG/T D21-2014). The length of the ramp weaving area was set to 500m. The lane width of the main road was set to 3.75m, with a speed limit of 100km/h, and the lane width of the ramp was set to 3.75m, with a speed limit of 40km/h.
(2)驾驶行为：(2) Driving behavior:
车辆的纵向运动采用了德国Karlsruhe大学Weidemann教授的“心理-生理跟车模型”，该模型分为Weidemann74和Weidemann99两种，本发明采用同时适用于公路驾驶环境和自动驾驶行为描述的Weidemann99，公式为：The longitudinal motion of the vehicle adopts the "psychological-physiological following model" of Professor Weidemann of Karlsruhe University in Germany. The model is divided into Weidemann74 and Weidemann99. The present invention adopts Weidemann99, which is suitable for both highway driving environment and automatic driving behavior description. The formula is:
dxsafe＝CC0+CC1×vdxsafe = CC0 + CC1 × v
式中，dxsafe表示平均行车安全距离，CC0表示停车间距；CC1表示车头时距，v为行车速度。In the formula, dxsafe represents the average safe driving distance, CC0 represents the parking distance, CC1 represents the headway, and v is the driving speed.
此外，自动驾驶货车的驾驶行为设定为可在专用道编队行驶。跟车模型采用由VISSIM标定的各类车型的默认参数。In addition, the driving behavior of the autonomous trucks is set to allow them to drive in formation on the dedicated lanes. The following vehicle model uses the default parameters of various vehicle types calibrated by VISSIM.
车辆换道一般行为主要包括自由车道选择和右行规则两种，模型基本参数则包括：最小车头时距、换道行为选择、最大减速度、可接受减速度等。通常在公路中车辆行驶遵循自由车道选择方式，该行为下人工驾驶车辆和自动驾驶车辆的换道行为模型均采用默认参数。The general behavior of vehicle lane change mainly includes free lane selection and right-driving rule. The basic parameters of the model include: minimum headway, lane change behavior selection, maximum deceleration, acceptable deceleration, etc. Usually, vehicles on the road follow the free lane selection method. Under this behavior, the lane change behavior models of manual and automatic driving vehicles use default parameters.
期望车速是指在车辆运行不受其他车辆干扰时，驾驶人所期望达到的行驶速度，其大小主要受驾驶人特性、车辆性能、道路条件三个因素的影响。期望速度决定了车辆进入路网的初始速度，而且它对车辆的超车和排队有重要的影响。设置主路期望车速的最大值为100km/h，最小值60km/h，匝道期望车速的最大值为40km/h，最小值为20km/h。The expected speed refers to the speed that the driver expects to reach when the vehicle is not disturbed by other vehicles. Its size is mainly affected by three factors: driver characteristics, vehicle performance, and road conditions. The expected speed determines the initial speed of the vehicle entering the road network, and it has an important impact on the overtaking and queuing of vehicles. The maximum value of the expected speed on the main road is set to 100km/h and the minimum value is 60km/h. The maximum value of the expected speed on the ramp is 40km/h and the minimum value is 20km/h.
(3)交通流条件：(3) Traffic flow conditions:
为全面覆盖各种交通需求水平，选取20％、40％、60％、80％、100％的饱和交通量作为路网交通流量输入。主路的饱和交通量为1800veh/h，匝道的饱和交通量为1200veh/h，入口匝道和出口匝道的交通流量输入占主路总流量的20％。In order to fully cover various traffic demand levels, 20%, 40%, 60%, 80%, and 100% saturated traffic volumes are selected as the road network traffic flow input. The saturated traffic volume of the main road is 1800 veh/h, the saturated traffic volume of the ramp is 1200 veh/h, and the traffic flow input of the entrance ramp and exit ramp accounts for 20% of the total traffic volume of the main road.
目前国内公路不同路段货车占比从2％～70％不等，但大部分货车占比集中于10％～50％区间，选取10％，20％，30％，40％和50％的货车占比作为评价模型中车型输入。At present, the proportion of trucks on different sections of domestic highways ranges from 2% to 70%, but most of the trucks are concentrated in the range of 10% to 50%. Trucks with proportions of 10%, 20%, 30%, 40% and 50% are selected as vehicle type inputs in the evaluation model.
考虑到自动驾驶技术逐步落地应用，选取10％、20％、30％、40％、50％的自动驾驶渗透率作为评价模型中的驾驶特征的输入。Taking into account the gradual implementation of autonomous driving technology, autonomous driving penetration rates of 10%, 20%, 30%, 40% and 50% are selected as the input of driving characteristics in the evaluation model.
(4)评价指标(4) Evaluation indicators
本发明将路网车均延误作为仿真评价指标，主要反映整个路网的通行效率，车均延误数值越高，表示路网整体通行效率越低。为了避免仿真时间过短导致结果失真，每一类专用车道设置方式在每一组交通流条件下的仿真时长均为7800s。为避免仿真初期车辆运行状态不稳定，影响仿真结果的准确性，仿真评价时间设置为600s～7800s。同时，为了保证仿真结果的稳定性，仿真过程中选择对每组仿真模型运行5次，将5次仿真输出结果的平均值作为最终仿真结果，每一次仿真运行选取的随机数分别为9、19、29、39、49。The present invention uses the average vehicle delay of the road network as a simulation evaluation index, which mainly reflects the traffic efficiency of the entire road network. The higher the average vehicle delay value, the lower the overall traffic efficiency of the road network. In order to avoid distortion of the results due to too short simulation time, the simulation time of each type of dedicated lane setting method under each set of traffic flow conditions is 7800s. In order to avoid the instability of the vehicle running state in the early stage of simulation and affect the accuracy of the simulation results, the simulation evaluation time is set to 600s to 7800s. At the same time, in order to ensure the stability of the simulation results, each group of simulation models is selected to run 5 times during the simulation process, and the average value of the 5 simulation output results is used as the final simulation result. The random numbers selected for each simulation run are 9, 19, 29, 39, and 49 respectively.
(5)实验结果(5) Experimental results
1)车均延误分布1) Average delay distribution per train
各类交通流条件下，三种专用车道设置方式的路网车均延误分布情况如图4所示。从图中可以看出，自动驾驶货车专用车道方案下发生路网车均延误过高(>600s)的频次远小于货车专用车道方案和无专用车道方案，表明自动驾驶货车专用车道方案下通行效率的稳定性整体更优。此外，路网车均延误明显随着交通量水平、货车占比的增大而提高，表明路网车均延误与交通量水平和货车占比两个自变量呈正相关关系。Under various traffic flow conditions, the distribution of average vehicle delays of the three dedicated lane settings is shown in Figure 4. As can be seen from the figure, the frequency of excessive average vehicle delays (>600s) in the autonomous driving truck dedicated lane scheme is much lower than that in the truck dedicated lane scheme and the no dedicated lane scheme, indicating that the stability of traffic efficiency under the autonomous driving truck dedicated lane scheme is better overall. In addition, the average vehicle delay of the road network increases significantly with the increase of traffic volume level and the proportion of trucks, indicating that the average vehicle delay of the road network is positively correlated with the two independent variables of traffic volume level and truck proportion.
2)方案对比分析2) Comparative analysis of solutions
本发明以交通量水平为饱和交通量的20％、60％和100％三类交通流条件为例，详细展示了三种专用车道设置方式的路网车均延误结果，如图5-7所示。The present invention takes three types of traffic flow conditions with traffic volume levels of 20%, 60% and 100% of the saturated traffic volume as examples, and shows in detail the average vehicle delay results of the road network for three dedicated lane settings, as shown in Figures 5-7.
如图5所示，展示了交通量水平为饱和交通量的20％条件下，三种专用车道设置方式的路网车均延误结果。从图5中可以看出，当自动驾驶渗透率低(10％)时，无专用车道方案的路网车均延误最小，表明在此条件下设置专用车道会对交通运行效率产生负面影响，增大路网车均延误。假定专用车道方案的路网车均延误大于无专用车道方案时，专用车道方案失效，可以发现除在自动驾驶渗透率高(30％～50％)且货车占比小(10％～30％)的情况外，货车专用车道方案均处于失效状态。此外，自动驾驶货车专用车道方案的车均延误均小于货车专用车道方案。As shown in Figure 5, the average vehicle delay results of the three dedicated lane settings are shown under the condition that the traffic volume level is 20% of the saturated traffic volume. As can be seen from Figure 5, when the penetration rate of autonomous driving is low (10%), the average vehicle delay of the road network without dedicated lanes is the smallest, indicating that setting up dedicated lanes under this condition will have a negative impact on traffic operation efficiency and increase the average vehicle delay of the road network. Assuming that the average vehicle delay of the dedicated lane solution is greater than that of the no dedicated lane solution, the dedicated lane solution is invalid. It can be found that except for the case where the penetration rate of autonomous driving is high (30% to 50%) and the proportion of trucks is small (10% to 30%), the truck dedicated lane solution is in an invalid state. In addition, the average vehicle delay of the autonomous driving truck dedicated lane solution is smaller than that of the truck dedicated lane solution.
如图6所示，展示了交通量水平为饱和交通量的60％条件下，三种专用车道设置方式的路网车均延误结果。从图6中可以看出，除货车占比低(10％～30％)的情况外，货车专用车道方案均处于严重失效状态；除个别自动驾驶渗透率高(40％～50％)的情况外，自动驾驶货车专用车道方案总体上优于货车专用车道方案，但仍然普遍处于失效状态。可见，在交通量中等的情况下，自动驾驶渗透率对专用车道设置的有效性起到关键作用，仅在自动驾驶渗透率接近半数(50％)时，适合设置专用车道(自动驾驶货车专用车道/货车专用车道)。As shown in Figure 6, the delay results of the three dedicated lane settings are shown under the condition of 60% of the saturated traffic volume. As can be seen from Figure 6, except for the case where the proportion of trucks is low (10% to 30%), the truck dedicated lane scheme is in a serious failure state; except for the case where the penetration rate of autonomous driving is high (40% to 50%), the autonomous driving truck dedicated lane scheme is generally better than the truck dedicated lane scheme, but it is still generally in a failure state. It can be seen that under medium traffic volume, the autonomous driving penetration rate plays a key role in the effectiveness of the dedicated lane setting. Only when the autonomous driving penetration rate is close to half (50%), it is suitable to set up dedicated lanes (autonomous driving truck dedicated lanes/truck dedicated lanes).
如图7所示，展示了交通量水平为饱和交通量的100％条件下，三种专用车道设置方式的路网车均延误结果。从图中可以看出，自动驾驶货车专用车道方案的路网车均延误均为最小，表明自动驾驶货车专用车道适用于交通量高的交通条件。此外，结合图6和图7可以看出，在交通量水平较高(60％～100％)且货车占比较大(40％～50％)时，货车专用车道方案的路网车均延误均大于450s，处于严重失效状态。As shown in Figure 7, the average delay results of the three dedicated lane settings are shown when the traffic volume level is 100% of the saturated traffic volume. It can be seen from the figure that the average delay of the road network vehicles in the dedicated lane for autonomous driving trucks is the smallest, indicating that the dedicated lane for autonomous driving trucks is suitable for traffic conditions with high traffic volume. In addition, combined with Figures 6 and 7, it can be seen that when the traffic volume level is high (60% to 100%) and the proportion of trucks is large (40% to 50%), the average delay of the road network vehicles in the dedicated lane for trucks is greater than 450s, which is in a serious failure state.
3)评价模型验证3) Evaluation model verification
为了验证本发明建立的评价模型对路网车均延误的拟合效果，本发明开展了其与多元线性模型和对数回归模型的对比分析。针对三种专用车道设置方法，数据拟合结果如图8所示。In order to verify the fitting effect of the evaluation model established by the present invention on the average vehicle delay of the road network, the present invention carried out a comparative analysis between the evaluation model and the multivariate linear model and the logarithmic regression model. For the three dedicated lane setting methods, the data fitting results are shown in Figure 8.
本发明采用均方根误差(Root Mean Square Error，RMSE)和拟合优度(R-squared，R2)两项指标反映数据拟合效果。RMSE反映了测量数据偏离真实值的程度，RMSE的值越小说明模型的拟合效果越好；R2反映了真实数据点聚集在回归线周围的密集程度，R2最大值为1，R2的值越接近1说明模型的拟合效果越好。The present invention uses two indicators, Root Mean Square Error (RMSE) and goodness of fit (R-squared, R2), to reflect the data fitting effect. RMSE reflects the degree to which the measured data deviates from the true value. The smaller the RMSE value, the better the model fitting effect; R2 reflects the density of the real data points gathered around the regression line. The maximum value of R2 is 1. The closer the value of R2 is to 1, the better the model fitting effect.
针对自动驾驶货车专用车道方案，Regarding the dedicated lane solution for autonomous trucks,
本发明建立的评价模型公式为：The evaluation model formula established by the present invention is:
D＝-25.78+3458.00×a1a2+1486.88×a1a3+1673.47×a1a4+1673.47×a2a3+1310.79×a2a4+910.83×a3a4D＝-25.78+3458.00×a1 a2 +1486.88×a1 a3 +1673.47×a1 a4 +1673.47×a2 a3 +1310.79×a2 a4 +910.83×a3 a4
多元线性模型公式为：The multivariate linear model formula is:
D＝-150.72+918.22×a1+1014.64×a2+697.02×a3+180.50×a4D＝-150.72+918.22×a1 +1014.64×a2 +697.02×a3 +180.50×a4
对数回归模型公式为：The logarithmic regression model formula is:
D＝716.77+48.91×Ln(a1)+77.95×Ln(a2)+71.26×Ln(a3)+42.23×Ln(a4)表1展示了三类模型对于自动驾驶货车专用车道方案的数据拟合结果。从表可以看出，本发明建立的评价模型对路网车均延误的拟合效果最佳，模型的RMSE最小，值为71.1190，R2最大，值为0.8458，即该模型可利用自动驾驶渗透率、交通量、货车占比可以解释路网车均延误的84.58％变化原因。模型的拟合效果显著优于对数回归模型，并在一定程度上优于多元线性模型，证明了本发明建立的评价模型对于自动驾驶专用车道方案的适用性。D＝716.77+48.91×Ln(a1 )+77.95×Ln(a2 )+71.26×Ln(a3 )+42.23×Ln(a4 )Table 1 shows the data fitting results of the three types of models for the dedicated lane scheme for autonomous driving trucks. It can be seen from the table that the evaluation model established by the present invention has the best fitting effect on the average delay of the road network vehicles. The RMSE of the model is the smallest, with a value of 71.1190, and the R2 is the largest, with a value of 0.8458. That is, the model can use the autonomous driving penetration rate, traffic volume, and truck proportion to explain 84.58% of the changes in the average delay of the road network vehicles. The fitting effect of the model is significantly better than the logarithmic regression model, and to a certain extent, it is better than the multivariate linear model, which proves the applicability of the evaluation model established by the present invention to the dedicated lane scheme for autonomous driving.
表1自动驾驶货车专用车道方案的数据拟合结果Table 1 Data fitting results of the dedicated lane scheme for autonomous driving trucks
表2(全量数据)展示了三类模型对于货车专用车道方案的数据拟合结果。从表可以看出多元线性回归模型对路网车均延误的拟合效果最佳，本发明建立的评价模型为次好，该模型可利用自动驾驶渗透率、交通量、货车占比可以解释路网车均延误的81.85％变化原因，但相较多元线性模型仍存在一定差距。结合图8展示的全量数据拟合结果和表3展示的部分数据拟合误差结果可以看出，当路网车均延误过高(即交通量水平超过60％饱和交通量且货车占比超过40％)时，本发明建立的评价模型较多元线性模型呈现劣势，若对剔除严重失效条件下的数据进行拟合，三类模型的数据拟合结果如表2(非严重失效情况下数据)所示，在该种情况下，本发明建立的评价模型仍为最优。Table 2 (full data) shows the data fitting results of the three types of models for the truck dedicated lane scheme. It can be seen from the table that the multivariate linear regression model has the best fitting effect on the average delay of the road network vehicles, and the evaluation model established by the present invention is the second best. The model can use the penetration rate of autonomous driving, traffic volume, and the proportion of trucks to explain 81.85% of the changes in the average delay of the road network vehicles, but there is still a certain gap compared with the multivariate linear model. Combining the full data fitting results shown in Figure 8 and the partial data fitting error results shown in Table 3, it can be seen that when the average delay of the road network vehicles is too high (that is, the traffic volume level exceeds 60% of the saturated traffic volume and the proportion of trucks exceeds 40%), the evaluation model established by the present invention is inferior to the multivariate linear model. If the data under the severe failure conditions are excluded, the data fitting results of the three types of models are shown in Table 2 (data under non-severe failure conditions). In this case, the evaluation model established by the present invention is still the best.
表2货车专用车道方案的数据拟合结果Table 2 Data fitting results of truck dedicated lane scheme
表3货车专用车道方案的部分数据绝对误差Table 3 Absolute error of some data of truck dedicated lane scheme
针对无专用道设置方案：For the solution without dedicated lanes:
本发明建立的评价模型公式为：The evaluation model formula established by the present invention is:
D＝-80.46+4235.67×a1a2+11980.24×a1a3-4409.17×a1a4-4409.16×a2a3+4856.35×a2a4+3129.042×a3a4D＝-80.46+4235.67×a1 a2 +11980.24×a1 a3 -4409.17×a1 a4 -4409.16×a2 a3 +4856.35×a2 a4 +3129.042×a3 a4
多元线性模型公式为：The multivariate linear model formula is:
D＝-302.63+1363.59×a1+1137.50×a2+919.09×a3+812.64×a4D＝-302.63+1363.59×a1 +1137.50×a2 +919.09×a3 +812.64×a4
对数回归模型公式为：The logarithmic regression model formula is:
D＝1118.74+31.21×Ln(a1)+117.26×Ln(a2)+91.42×Ln(a3)+177.46×Ln(a4)D＝1118.74+31.21×Ln(a1 )+117.26×Ln(a2 )+91.42×Ln(a3 )+177.46×Ln(a4 )
表4展示了三类模型对于无专用车道方案的数据拟合结果。从表可以看出，本发明建立的评价模型对路网车均延误的拟合效果最佳，模型的RMSE最小，值为102.2168，R2最大，值为0.8832，即该模型可利用自动驾驶渗透率、交通量、货车占比可以解释路网车均延误的88.32％变化原因。模型的拟合效果显著优于对数回归模型，并在一定程度上优于多元线性回归模型，证明了本发明建立的评价模型对于自动驾驶专用车道的适用性。Table 4 shows the data fitting results of the three types of models for the scheme without dedicated lanes. It can be seen from the table that the evaluation model established by the present invention has the best fitting effect on the average delay of the road network vehicles. The RMSE of the model is the smallest, with a value of 102.2168, and the R2 is the largest, with a value of 0.8832. That is, the model can use the autonomous driving penetration rate, traffic volume, and the proportion of trucks to explain 88.32% of the changes in the average delay of the road network vehicles. The fitting effect of the model is significantly better than the logarithmic regression model, and to a certain extent, it is better than the multivariate linear regression model, which proves the applicability of the evaluation model established by the present invention for autonomous driving dedicated lanes.
表4无专用车道方案的数据拟合结果Table 4 Data fitting results of the scheme without dedicated lanes
4)最优方案集4) Optimal solution set
如图9所示，展示了各类交通条件下三种专用车道设置方式中路网车均延误最小的方案集。从图中可以看出，对于典型的单向四车道公路路段，交通量水平相对低(20％～60％)且自动驾驶渗透率相对低(10％～30％)时，多数情况不宜设置专用车道；交通量水平相对低(20％～60％)且自动驾驶渗透率相对高(40％～50％)时，货车专用车道方案适合货车占比低(10％～20％)的交通条件，自动驾驶专用车道方案适合货车占比较高(20％～50％)的交通条件；交通量水平较高(80％～100％)时，则普遍适合设置自动驾驶货车专用车道。As shown in Figure 9, the scheme set with the smallest average delay for all vehicles in the three dedicated lane settings under various traffic conditions is shown. As can be seen from the figure, for a typical one-way four-lane highway section, when the traffic volume level is relatively low (20% to 60%) and the penetration rate of autonomous driving is relatively low (10% to 30%), it is not appropriate to set up dedicated lanes in most cases; when the traffic volume level is relatively low (20% to 60%) and the penetration rate of autonomous driving is relatively high (40% to 50%), the truck dedicated lane scheme is suitable for traffic conditions with a low proportion of trucks (10% to 20%), and the autonomous driving dedicated lane scheme is suitable for traffic conditions with a high proportion of trucks (20% to 50%); when the traffic volume level is high (80% to 100%), it is generally suitable to set up dedicated lanes for autonomous driving trucks.
5)仿真结果小结5) Summary of simulation results
利用VISSIM及其COM二次开发接口，建立了典型的单向四车道公路路段，构建了由20％～100％饱和交通量、10％～50％货车占比和10％～50％自动驾驶渗透率组成的375组交通流条件，得出了各交通流条件下的路网车均延误。通过分析车均延误分布，发现自动驾驶货车专用车道方案的通行效率的稳定性整体更优；通过对比各专用车道设置方式的车均延误，分析了对交通流条件的适应性，发现并非所有交通流条件均适合设置专用车道，且在一定条件下货车专用车道处于严重失效状态；利用本发明建立的评价模型对车均延误数据做了拟合，与多元线性模型、对数回归模型对比发现，本发明提出的评价模型具有明显的优势；通过归纳路网车均延误最小的专用车道设置方案集，为将来实际路网中相应的交通流条件下的专用车道设置提供参考。Using VISSIM and its COM secondary development interface, a typical one-way four-lane highway section was established, and 375 groups of traffic flow conditions consisting of 20% to 100% saturated traffic volume, 10% to 50% truck proportion and 10% to 50% autonomous driving penetration rate were constructed, and the average vehicle delay of the road network under each traffic flow condition was obtained. By analyzing the average vehicle delay distribution, it is found that the stability of the traffic efficiency of the dedicated lane scheme for autonomous driving trucks is better overall; by comparing the average vehicle delay of each dedicated lane setting method, the adaptability to traffic flow conditions is analyzed, and it is found that not all traffic flow conditions are suitable for setting up dedicated lanes, and under certain conditions, the dedicated lanes for trucks are in a serious failure state; the evaluation model established by the present invention is used to fit the average vehicle delay data, and compared with the multivariate linear model and the logarithmic regression model, it is found that the evaluation model proposed by the present invention has obvious advantages; by summarizing the dedicated lane setting scheme set with the smallest average vehicle delay in the road network, a reference is provided for the setting of dedicated lanes under corresponding traffic flow conditions in the actual road network in the future.
本发明的优点：Advantages of the present invention:
本发明考虑到未来自动驾驶车辆的发展，人工驾驶客车、人工驾驶货车与自动驾驶客车、自动驾驶货车组成的混合交通流更为复杂，提供自动驾驶车辆混入下的公路货车专用道设置条件评价方法，构建以交通量水平、货车占比和自动驾驶渗透率为影响因素的车均延误模型，并以不同交通流条件来标定车均延误模型的参数，应用标定后的车均延误模型计算路网车均延误，车均延误越大路网通行效率越低，基于计算得到的路网车均延误对比不同专用车道设置方式的路网通行效率，得出不同专用车道设置方式下各因素对路网车均延误的影响趋势，以及各类交通条件下最适用的专用车道设置方式，最终，提高公路通行效率。The present invention takes into account the development of autonomous driving vehicles in the future. The mixed traffic flow consisting of manually driven buses, manually driven trucks, autonomous driving buses and autonomous driving trucks is more complicated. The present invention provides a method for evaluating the setting conditions of highway truck lanes under the mixing of autonomous driving vehicles, constructs an average vehicle delay model with traffic volume level, truck proportion and autonomous driving penetration rate as influencing factors, calibrates the parameters of the average vehicle delay model with different traffic flow conditions, and uses the calibrated average vehicle delay model to calculate the average vehicle delay of the road network. The greater the average vehicle delay, the lower the road network traffic efficiency. Based on the calculated average vehicle delay of the road network, the road network traffic efficiency of different dedicated lane setting methods is compared, and the influence trend of various factors on the average vehicle delay of the road network under different dedicated lane setting methods is obtained, as well as the most suitable dedicated lane setting method under various traffic conditions, ultimately improving the highway traffic efficiency.
以上仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
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